diff --git a/docs/docs/examples/examples/patient_form_extraction.md b/docs/docs/examples/examples/patient_form_extraction.md
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--- a/docs/docs/examples/examples/patient_form_extraction.md
+++ b/docs/docs/examples/examples/patient_form_extraction.md
@@ -1,6 +1,6 @@
---
title: Extract Nested Structured Data from Patient Form
-description: Extract nested structured data from patient form
+description: Extract nested structured data from patient form and performs data mapping and field level transformation.
sidebar_class_name: hidden
slug: /examples/patient_form_extraction
canonicalUrl: '/examples/patient_form_extraction'
@@ -209,7 +209,7 @@ with data_scope["documents"].row() as doc:
-After the extraction, we collect all the fields for simplicity. You can also select any fields and also perform data mapping and field level transformation on the fields before the collection. If you have any questions, feel free to ask us in [Discord](https://discord.com/invite/zpA9S2DR7s).
+After the extraction, we collect all the fields for simplicity. You can also select any fields and also perform data mapping and field level transformation on the fields before the collection.
## Export the extracted data to a table
diff --git a/docs/docs/examples/examples/patient_form_extraction_baml.md b/docs/docs/examples/examples/patient_form_extraction_baml.md
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+++ b/docs/docs/examples/examples/patient_form_extraction_baml.md
@@ -0,0 +1,305 @@
+---
+title: Extracting Intake Forms with BAML and CocoIndex
+description: How to use BAML together with CocoIndex to build a data pipeline that extracts structured patient information from PDF intake forms.
+sidebar_class_name: hidden
+slug: /examples/patient_form_extraction_baml
+canonicalUrl: '/examples/patient_form_extraction_baml'
+sidebar_custom_props:
+ image: /img/examples/patient_form_extraction_baml/cover.png
+ tags: [structured-data-extraction, custom-building-blocks ]
+image: /img/examples/patient_form_extraction_baml/cover.png
+tags: [structured-data-extraction, custom-building-blocks]
+---
+
+import { GitHubButton, YouTubeButton, DocumentationButton } from '../../../src/components/GitHubButton';
+
+
+
+
+
+## Overview
+
+
+This tutorial shows how to use BAML together with CocoIndex to build a data pipeline that extracts structured patient information from PDF intake forms. The BAML definitions describe the desired output schema and prompt logic, while CocoIndex orchestrates file input, transformation, and incremental indexing.
+
+:::info
+The extraction quality is highly dependent on the OCR quality. You can use CocoIndex with any commercial parser or open source ones that is tailored for your domain for better results. For example, Document AI from Google Cloud and more.
+:::
+
+## Flow Overview
+
+
+
+The flow itself is fairly simple.
+
+1. Read PDF files from a directory.
+2. For each file, call the BAML function to get a structured `Patient`.
+3. Collect results and export to Postgres.
+
+## Setup
+
+1. [Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one.
+2. Install dependencies
+
+ ```
+ pip install -U cocoindex baml-py
+ ```
+3. Create a `.env` file. You can copy it from `.env.example` first:
+
+ ```
+ cp .env.example .env
+ ```
+
+ Then edit the file to fill in your `GEMINI_API_KEY`.
+
+
+## Structured Extraction Component with BAML
+
+Create a `baml_src/` directory for your BAML definitions. We’ll define a schema for patient intake data (nested classes) and a function that prompts Gemini to extract those fields from a PDF. Save this as `baml_src/patient.baml`
+
+### Define Patient Schema
+
+**Classes**: We defined Pydantic-style classes (`Contact`, `Address`, `Insurance`, etc.) to match the FHIR-inspired patient schema. These become typed output models. Required fields are non-nullable; optional fields use `?`.
+
+
+
+```python
+class Contact {
+ name string
+ phone string
+ relationship string
+}
+
+class Address {
+ street string
+ city string
+ state string
+ zip_code string
+}
+
+class Pharmacy {
+ name string
+ phone string
+ address Address
+}
+
+class Insurance {
+ provider string
+ policy_number string
+ group_number string?
+ policyholder_name string
+ relationship_to_patient string
+}
+
+class Condition {
+ name string
+ diagnosed bool
+}
+
+class Medication {
+ name string
+ dosage string
+}
+
+class Allergy {
+ name string
+}
+
+class Surgery {
+ name string
+ date string
+}
+
+class Patient {
+ name string
+ dob string
+ gender string
+ address Address
+ phone string
+ email string
+ preferred_contact_method string
+ emergency_contact Contact
+ insurance Insurance?
+ reason_for_visit string
+ symptoms_duration string
+ past_conditions Condition[]
+ current_medications Medication[]
+ allergies Allergy[]
+ surgeries Surgery[]
+ occupation string?
+ pharmacy Pharmacy?
+ consent_given bool
+ consent_date string?
+}
+```
+
+### Define the BAML function to extract patient info from a PDF
+
+```python
+function ExtractPatientInfo(intake_form: pdf) -> Patient {
+ client Gemini
+ prompt #"
+ Extract all patient information from the following intake form document.
+ Please be thorough and extract all available information accurately.
+
+ {{ intake_form }}
+
+ Fill in with "N/A" for required fields if the information is not available.
+
+ {{ ctx.output_format }}
+ "#
+}
+```
+
+We specify `client Gemini` and a prompt template. The special variable `{{ intake_form }}` injects the PDF, and `{{ ctx.output_format }}` tells BAML to expect the structured format defined by the return type. The prompt explicitly asks Gemini to extract all fields, filling “N/A” if missing.
+
+## Configure the LLM client to use Google’s Gemini model
+
+```python
+client Gemini {
+ provider google-ai
+ options {
+ model gemini-2.5-flash
+ api_key env.GEMINI_API_KEY
+ }
+}
+```
+
+### Configure BAML generator
+
+In `baml_src` folder add `generator.baml`
+
+```python
+generator python_client {
+ output_type python/pydantic
+ output_dir "../"
+ version "0.213.0"
+}
+```
+
+The `generator` block tells `baml-cli` to create a Python client with Pydantic models in the parent directory.
+
+When we run `baml-cli generate`
+
+This will compile the `.baml` definitions into a `baml_client/` Python package in your project root. It contains:
+
+- `baml_client/types.py` with Pydantic classes (`Patient`, etc.).
+- `baml_client/sync_client.py` and `async_client.py` with a callable `b` object. For example, `b.ExtractPatientInfo(pdf)` will return a `Patient`.
+
+## Alternative - Native ExtractByLLM Component
+If you prefer to define the extraction logic in a native CocoIndex function with native Python class, you could also use the `ExtractByLLM` component.
+
+
+
+You could see an example [here](/examples/patient_form_extraction).
+
+
+## Continuous Data Transformation flow with incremental processing
+
+Next we will define data transformation flow with CocoIndex. Once you declared the state and transformation logic, CocoIndex will take care of all the state change for you from source to target.
+
+### CocoIndex Flow
+
+#### Declare Flow
+
+Declare a CocoIndex flow, connect to the source, add a data collector to collect processed data.
+
+```python
+@cocoindex.flow_def(name="PatientIntakeExtractionBaml")
+def patient_intake_extraction_flow(
+ flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope
+) -> None:
+ data_scope["documents"] = flow_builder.add_source(
+ cocoindex.sources.LocalFile(
+ path=os.path.join("data", "patient_forms"), binary=True
+ )
+ )
+
+ patients_index = data_scope.add_collector()
+```
+
+This iterates over each document. We transform `doc["content"]` (the bytes) by our `extract_patient_info` function. The result is stored in a new field `patient_info`. Then we collect a row with the filename and extracted patient info.
+
+
+
+#### Define a custom function to use BAML extraction to transform a PDF
+
+```python
+@cocoindex.op.function(cache=True, behavior_version=1)
+async def extract_patient_info(content: bytes) -> Patient:
+ pdf = baml_py.Pdf.from_base64(base64.b64encode(content).decode("utf-8"))
+ return await b.ExtractPatientInfo(pdf)
+```
+
+- `@cocoindex.op.function(cache=True, behavior_version=1)` caches results for incremental processing; bump `behavior_version` to refresh cache if logic changes.
+- The function base64-encodes input bytes, creates a BAML `Pdf`, and calls `await b.ExtractPatientInfo(pdf)` to return a `Patient` object.
+
+
+
+
+#### Process each document
+
+1. Transform each doc with BAML
+2. collect the structured output
+
+```python
+with data_scope["documents"].row() as doc:
+ doc["patient_info"] = doc["content"].transform(extract_patient_info)
+
+ patients_index.collect(
+ filename=doc["filename"],
+ patient_info=doc["patient_info"],
+ )
+```
+
+
+
+It is common to have heavy nested data, CocoIndex is natively designed to handle heavily nested data structures.
+
+
+
+### Export to Postgres
+
+```python
+patients_index.export(
+ "patients",
+ cocoindex.storages.Postgres(),
+ primary_key_fields=["filename"],
+)
+```
+
+Exports the index to Postgres as the `patients` table, with automatic updates and deletions when source files change.
+
+## Running the Pipeline
+
+**Generate BAML client code** (required step, in case you didn’t do it earlier. )
+
+```bash
+baml generate
+```
+
+This generates the `baml_client/` directory with Python code to call your BAML functions.
+
+Update the index:
+
+```bash
+cocoindex update main
+```
+
+**CocoInsight**
+
+I used CocoInsight (Free beta now) to troubleshoot the index generation and understand the data lineage of the pipeline. It just connects to your local CocoIndex server, with zero pipeline data retention.
+
+```bash
+cocoindex server -ci main
+```
+
+## Composable by Default: Use the Best Components for Your Use Case
+
+CocoIndex offers a comprehensive toolkit for building robust LLM pipelines, but it's intentionally built as an open and interoperable system. You can seamlessly incorporate your own preferred components—such as custom document parsers or structured extraction tools like BAML—according to your domain requirements.
+
+## Connect to other sources
+
+CocoIndex natively supports Google Drive, Amazon S3, Azure Blob Storage, and more.
+
+
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